Getting bounding boxes on words instead of letters
0
I am trying to put bounding boxes on each word which are there in a document. I have succeeded in putting bounding boxes on more or less each letter, but am unable to put bounding boxes on each word. I am using the code below for the same
im_ns = cv.imread('image.jpg')
gray = cv.cvtColor(im_ns,cv.COLOR_BGR2GRAY)
blurred_g = cv.GaussianBlur(gray,(11,11),0)
ret, th1 = cv.threshold(blurred_g,127,255,cv.THRESH_BINARY)
th2 = cv.adaptiveThreshold(blurred_g,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,2)
th3 = cv.adaptiveThreshold(blurred_g,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)
th3_1 = cv.adaptiveThreshold(th3,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)
plt.figure(figsize=(30,25))
plt.imshow(th3_1)
th3_er = cv.erode(th3_1,None,iterations=1)
th3_di = cv.dilate(th3_er,None,iterations=1)
labels = measure.label(th3_di1, neighbors=8, background=255)
mask = np.zeros(th3_di1.shape, dtype="uint8")
for lab in np.unique(labels):
if lab == 0:
continue
labelMask = np.zeros(th3_di.shape, dtype="uint8")
labelMask[labels == lab] = 255
numPixels = cv.countNonZero(labelMask)
if numPixels > 13:
mask = cv.add(mask, labelMask)
cnts = cv.findContours(mask.copy(), cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = contours.sort_contours(cnts)[0]
t = th3_di.copy()
for (i, c) in enumerate(cnts):
(x,y,w,h) = cv.boundingRect(c)
cv.rectangle(t,(x,y),(x+w,y+h),(0,255),2)
cv.imshow("Image", t)
cv.waitKey(10000)
cv.destroyAllWindows()
The output of the above code can be seen in the link below
opencv image-processing image-segmentation
asked Nov 22 at 16:07
Arindam Bose
283
add a comment |
0
I am trying to put bounding boxes on each word which are there in a document. I have succeeded in putting bounding boxes on more or less each letter, but am unable to put bounding boxes on each word. I am using the code below for the same
im_ns = cv.imread('image.jpg')
gray = cv.cvtColor(im_ns,cv.COLOR_BGR2GRAY)
blurred_g = cv.GaussianBlur(gray,(11,11),0)
ret, th1 = cv.threshold(blurred_g,127,255,cv.THRESH_BINARY)
th2 = cv.adaptiveThreshold(blurred_g,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,2)
th3 = cv.adaptiveThreshold(blurred_g,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)
th3_1 = cv.adaptiveThreshold(th3,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)
plt.figure(figsize=(30,25))
plt.imshow(th3_1)
th3_er = cv.erode(th3_1,None,iterations=1)
th3_di = cv.dilate(th3_er,None,iterations=1)
labels = measure.label(th3_di1, neighbors=8, background=255)
mask = np.zeros(th3_di1.shape, dtype="uint8")
for lab in np.unique(labels):
if lab == 0:
continue
labelMask = np.zeros(th3_di.shape, dtype="uint8")
labelMask[labels == lab] = 255
numPixels = cv.countNonZero(labelMask)
if numPixels > 13:
mask = cv.add(mask, labelMask)
cnts = cv.findContours(mask.copy(), cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = contours.sort_contours(cnts)[0]
t = th3_di.copy()
for (i, c) in enumerate(cnts):
(x,y,w,h) = cv.boundingRect(c)
cv.rectangle(t,(x,y),(x+w,y+h),(0,255),2)
cv.imshow("Image", t)
cv.waitKey(10000)
cv.destroyAllWindows()
The output of the above code can be seen in the link below
opencv image-processing image-segmentation
asked Nov 22 at 16:07
Arindam Bose
283
Maybe you can do morphological dilation to merge the letters using a small structure element. Then the connected components are words. Since there is blank space between words, it's less likely that different words are merged.
– Kaiwen Chang
Nov 23 at 13:47
add a comment |
0
0
0
I am trying to put bounding boxes on each word which are there in a document. I have succeeded in putting bounding boxes on more or less each letter, but am unable to put bounding boxes on each word. I am using the code below for the same
im_ns = cv.imread('image.jpg')
gray = cv.cvtColor(im_ns,cv.COLOR_BGR2GRAY)
blurred_g = cv.GaussianBlur(gray,(11,11),0)
ret, th1 = cv.threshold(blurred_g,127,255,cv.THRESH_BINARY)
th2 = cv.adaptiveThreshold(blurred_g,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,2)
th3 = cv.adaptiveThreshold(blurred_g,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)
th3_1 = cv.adaptiveThreshold(th3,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)
plt.figure(figsize=(30,25))
plt.imshow(th3_1)
th3_er = cv.erode(th3_1,None,iterations=1)
th3_di = cv.dilate(th3_er,None,iterations=1)
labels = measure.label(th3_di1, neighbors=8, background=255)
mask = np.zeros(th3_di1.shape, dtype="uint8")
for lab in np.unique(labels):
if lab == 0:
continue
labelMask = np.zeros(th3_di.shape, dtype="uint8")
labelMask[labels == lab] = 255
numPixels = cv.countNonZero(labelMask)
if numPixels > 13:
mask = cv.add(mask, labelMask)
cnts = cv.findContours(mask.copy(), cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = contours.sort_contours(cnts)[0]
t = th3_di.copy()
for (i, c) in enumerate(cnts):
(x,y,w,h) = cv.boundingRect(c)
cv.rectangle(t,(x,y),(x+w,y+h),(0,255),2)
cv.imshow("Image", t)
cv.waitKey(10000)
cv.destroyAllWindows()
The output of the above code can be seen in the link below
opencv image-processing image-segmentation
asked Nov 22 at 16:07
Arindam Bose
283
I am trying to put bounding boxes on each word which are there in a document. I have succeeded in putting bounding boxes on more or less each letter, but am unable to put bounding boxes on each word. I am using the code below for the same
im_ns = cv.imread('image.jpg')
gray = cv.cvtColor(im_ns,cv.COLOR_BGR2GRAY)
blurred_g = cv.GaussianBlur(gray,(11,11),0)
ret, th1 = cv.threshold(blurred_g,127,255,cv.THRESH_BINARY)
th2 = cv.adaptiveThreshold(blurred_g,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,2)
th3 = cv.adaptiveThreshold(blurred_g,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)
th3_1 = cv.adaptiveThreshold(th3,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)
plt.figure(figsize=(30,25))
plt.imshow(th3_1)
th3_er = cv.erode(th3_1,None,iterations=1)
th3_di = cv.dilate(th3_er,None,iterations=1)
labels = measure.label(th3_di1, neighbors=8, background=255)
mask = np.zeros(th3_di1.shape, dtype="uint8")
for lab in np.unique(labels):
if lab == 0:
continue
labelMask = np.zeros(th3_di.shape, dtype="uint8")
labelMask[labels == lab] = 255
numPixels = cv.countNonZero(labelMask)
if numPixels > 13:
mask = cv.add(mask, labelMask)
cnts = cv.findContours(mask.copy(), cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = contours.sort_contours(cnts)[0]
t = th3_di.copy()
for (i, c) in enumerate(cnts):
(x,y,w,h) = cv.boundingRect(c)
cv.rectangle(t,(x,y),(x+w,y+h),(0,255),2)
cv.imshow("Image", t)
cv.waitKey(10000)
cv.destroyAllWindows()
The output of the above code can be seen in the link below
opencv image-processing image-segmentation
opencv image-processing image-segmentation
asked Nov 22 at 16:07
Arindam Bose
283
asked Nov 22 at 16:07
Arindam Bose
283
asked Nov 22 at 16:07
Arindam Bose
283
asked Nov 22 at 16:07
Arindam Bose
283
asked Nov 22 at 16:07
Arindam Bose
283
283
Maybe you can do morphological dilation to merge the letters using a small structure element. Then the connected components are words. Since there is blank space between words, it's less likely that different words are merged.
– Kaiwen Chang
Nov 23 at 13:47
add a comment |
Maybe you can do morphological dilation to merge the letters using a small structure element. Then the connected components are words. Since there is blank space between words, it's less likely that different words are merged.
– Kaiwen Chang
Nov 23 at 13:47
Maybe you can do morphological dilation to merge the letters using a small structure element. Then the connected components are words. Since there is blank space between words, it's less likely that different words are merged.
– Kaiwen Chang
Nov 23 at 13:47
Maybe you can do morphological dilation to merge the letters using a small structure element. Then the connected components are words. Since there is blank space between words, it's less likely that different words are merged.
– Kaiwen Chang
Nov 23 at 13:47
add a comment |
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Maybe you can do morphological dilation to merge the letters using a small structure element. Then the connected components are words. Since there is blank space between words, it's less likely that different words are merged.
– Kaiwen Chang
Nov 23 at 13:47